Optimization has historically been performed by using so-called “Rules-of-Thumb” provided by vendors and industry experts. The techniques for optimization are general and do not typically account for the unique properties of the system to be optimized. In conjunction with or in place of industry best-practices is the “trial and error” technique where a change is made and a determination is made on whether the effect was in the optimal direction. “Trial and error” techniques are performed by changing only a few parameters, and the effect on the overall optimization is subjectively determined, as it is not known whether any “in-between” adjustment may have produced a better result. Ultimately, both techniques suffer from a high potential to miss the absolute optimal settings and are very time consuming. A technique that measures the response of a system under various configurations and analyzes the result in a methodical manner is needed to achieve consistent, reliable, repeatable, and optimal results.